// nnet3/am-nnet-simple.cc

// Copyright 2012-2015  Johns Hopkins University (author:  Daniel Povey)

// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//  http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.

#include "stdafx.h"
#include "am-nnet-simple.h"
#include "nnet-utils.h"

namespace kaldi {
namespace nnet3 {

//
//
//int32 AmNnetSimple::NumPdfs() const {
//  int32 ans = nnet_.OutputDim("output");
//  KALDI_ASSERT(ans > 0);
//  return ans;
//}
//
//void AmNnetSimple::Write(std::ostream &os, bool binary) const {
//  // We don't write any header or footer like <AmNnetSimple> and </AmNnetSimple> -- we just
//  // write the neural net and then the priors.  Who knows, there might be some
//  // situation where we want to just read the neural net.
//  nnet_.Write(os, binary);
//  WriteToken(os, binary, "<LeftContext>");
//  WriteBasicType(os, binary, left_context_);
//  WriteToken(os, binary, "<RightContext>");
//  WriteBasicType(os, binary, right_context_);
//  WriteToken(os, binary, "<Priors>");
//  priors_.Write(os, binary);
//}

void AmNnetSimple::Read(std::istream &is, bool binary) {
  nnet_.Read(is, binary);
  ExpectToken(is, binary, "<LeftContext>");
  ReadBasicType(is, binary, &left_context_);
  ExpectToken(is, binary, "<RightContext>");
  ReadBasicType(is, binary, &right_context_);
  //SetContext();  // temporarily, I'm not trusting the written ones (there was
                 // briefly a bug)
  ExpectToken(is, binary, "<Priors>");
  priors_.Read(is, binary);
}

//void AmNnetSimple::SetNnet(const Nnet &nnet) {
//  nnet_ = nnet;
//  SetContext();
//  if (priors_.Dim() != 0 && priors_.Dim() != nnet_.OutputDim("output")) {
//    KALDI_WARN << "Removing priors since there is a dimension mismatch after "
//               << "changing the nnet: " << priors_.Dim() << " vs. "
//               << nnet_.OutputDim("output");
//    priors_.Resize(0);
//  }
//}

//void AmNnetSimple::SetPriors(const VectorBase<BaseFloat> &priors) {
//  priors_ = priors;
//  if (priors_.Dim() != nnet_.OutputDim("output") &&
//      priors_.Dim() != 0) {
//    KALDI_ERR << "Dimension mismatch when setting priors: priors have dim "
//              << priors.Dim() << ", model expects "
//              << nnet_.OutputDim("output");
//  }
//}
//
//std::string AmNnetSimple::Info() const {
//  std::ostringstream ostr;
//  ostr << "input-dim: " << nnet_.InputDim("input") << "\n";
//  ostr << "ivector-dim: " << nnet_.InputDim("ivector") << "\n";
//  ostr << "num-pdfs: " << nnet_.OutputDim("output") << "\n";
//  ostr << "prior-dimension: " << priors_.Dim() << "\n";
//  if (priors_.Dim() != 0) {
//    ostr << "prior-sum: " << priors_.Sum() << "\n";
//    ostr << "prior-min: " << priors_.Min() << "\n";
//    ostr << "prior-max: " << priors_.Max() << "\n";
//  }
//  ostr << "# Nnet info follows.\n";
//  return ostr.str() + nnet_.Info();
//}
//
//
//void AmNnetSimple::SetContext() {
//  if (!IsSimpleNnet(nnet_)) {
//    KALDI_ERR << "Class AmNnetSimple is only intended for a restricted type of "
//              << "nnet, and this one does not meet the conditions.";
//  }
//  ComputeSimpleNnetContext(nnet_,
//                           &left_context_,
//                           &right_context_);
//}


} // namespace nnet3
} // namespace kaldi
